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虚假数据注入攻击信号的融合估计

翁品迪 陈博 俞立

翁品迪, 陈博, 俞立. 虚假数据注入攻击信号的融合估计. 自动化学报, 2021, 47(9): 2292−2300 doi: 10.16383/j.aas.c190045
引用本文: 翁品迪, 陈博, 俞立. 虚假数据注入攻击信号的融合估计. 自动化学报, 2021, 47(9): 2292−2300 doi: 10.16383/j.aas.c190045
Weng Pin-Di, Chen Bo, Yu Li. Fusion estimate of FDI attack signals. Acta Automatica Sinica, 2021, 47(9): 2292−2300 doi: 10.16383/j.aas.c190045
Citation: Weng Pin-Di, Chen Bo, Yu Li. Fusion estimate of FDI attack signals. Acta Automatica Sinica, 2021, 47(9): 2292−2300 doi: 10.16383/j.aas.c190045

虚假数据注入攻击信号的融合估计

doi: 10.16383/j.aas.c190045
基金项目: 国家自然科学基金(61673351, 61973277)资助
详细信息
    作者简介:

    翁品迪:浙江工业大学硕士研究生. 主要研究方向为信息物理系统中攻击信号的融合检测. E-mail: pwd2gg@aliyun.com

    陈博:浙江工业大学教授. 主要研究方向为信息融合, 安全估计与控制, 信息物理系统. 本文通信作者. E-mail: bchen@aliyun.com

    俞立:浙江工业大学教授. 主要研究方向为网络化控制, 信息融合, 信息物理系统. E-mail: lyu@zjut.edu.cn

Fusion Estimate of FDI Attack Signals

Funds: Supported by National Natural Science Foundation of China (61673351, 61973277)
More Information
    Author Bio:

    WENG Pin-Di Master student at Zhejiang University of Technology. His research interest covers fusion detection of attack signal in cyber physical system

    CHEN Bo Professor at Zhejiang University of Technology. His research interest covers information fusion, security estimate and control, and cyber physical system. Corresponding author of this paper

    YU Li Professor at Zhejiang University of Technology. His research interest covers networked control, information fusion, and cyber physical system

  • 摘要: 研究了信息物理系统中假数据注入(False data injection, FDI)攻击信号的检测问题. 在分布式融合框架下, 首先将FDI攻击信号建模为信息物理系统模型中的未知输入, 从而使得攻击信号的检测问题转化为对FDI攻击信号的实时估计问题. 其次, 在每个传感器端设计基于自适应卡尔曼滤波的FDI攻击信号的局部估计器; 在融合中心端引入补偿因子, 设计分布式信息融合准则以导出攻击信号的融合估计器. 特别地, 当FDI攻击信号是时变情况时, 融合过程中补偿因子的引入可以大大提高对攻击信号的估计精度. 最后, 通过两个仿真算例验证所提算法的有效性.
  • 图  1  算例1: 情况1中攻击信号和融合估计的轨迹

    Fig.  1  Example 1: The trajectories of attack signal and its fusion estimation under Case 1

    图  2  算例1: 情况1中攻击信号的局部估计器与融合估计器之间的性能比较

    Fig.  2  Example 1: The performance comparison between local estimators and fusion estimators under Case 1

    图  3  算例1: 情况2中攻击信号和融合估计轨迹

    Fig.  3  Example 1: The trajectories of attack signal and its fusion estimation under Case 2

    图  4  算例1: 情况2中攻击信号的局部估计器与融合估计器之间的性能比较

    Fig.  4  Example 1: The performance comparison between local estimators and fusion estimators under Case 2

    图  5  算例1: 情况2中不同补偿因子下攻击信号融合估计性能的比较

    Fig.  5  Example 1: The comparison of fusion estimation performance of attack signal under different compensation factors under Case 2

    图  6  算例1: 情况2中不同补偿因子下攻击信号融合估计的轨迹

    Fig.  6  Example 1: The trajectories of fusion estimation of attack signal under different compensation factors under Case 2

    图  7  算例2: 不同补偿因子下攻击信号融合估计的轨迹

    Fig.  7  Example 2: The trajectories of fusion estimation of attack signal under different compensation factors

    图  8  算例2: 不同补偿因子下攻击信号融合估计性能的比较

    Fig.  8  Example 2: The comparison of fusion estimation performance of attack signal under different compensation factors

    图  9  算例2: 攻击信号的局部估计器与融合估计器之间的性能比较

    Fig.  9  Example 2: The performance comparison of attack signal between local estimators and fusion estimators

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出版历程
  • 收稿日期:  2019-01-18
  • 录用日期:  2019-07-10
  • 网络出版日期:  2021-10-13
  • 刊出日期:  2021-10-13

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